Director of AI Engineering

Location : Remote - United States only.
About the role
Ottimate is building the AI-native future of accounts payable. Our platform processes millions of invoices across hundreds of enterprise customers, powered by a suite of ML models and agentic workflows. As Director of AI Engineering, you will own the full AI and ML layer of our product — from invoice understanding and vendor intelligence to our conversational AP Copilot and the next generation of autonomous AP agents.
This is a hands-on leadership role. You will spend at least half your time writing code, architecting systems, and driving technical decisions alongside your team. You will also set the AI roadmap, partner cross-functionally with Product, Data, and Platform Engineering, and manage a distributed team of 8–10 engineers across Data and ML.
We are looking for a senior technical manager or director — ideally someone who has thrived at a smaller company and is ready for a career step up into broader ownership. If you are energized by shipping real AI products, working with noisy real-world financial data, and building the systems that will define how enterprises automate AP, this role is for you.
Responsibilities
Technical Leadership
- Architect and ship production AI/ML systems — you write code, not just review it
- Own the AI roadmap end-to-end: prioritization, trade-offs, delivery
- Set technical standards for model quality, evals, observability, and reliability
- Drive adoption of agentic coding tools to multiply team velocity
- Claude Code, Cursor, Copilot, or equivalent — measure and improve PR throughput
- Partner with Platform Engineering on infrastructure, data pipelines, and APIs

People & Cross-Functional
- Manage a distributed team of 8–10 engineers across Data and ML disciplines
- Hire, develop, and retain engineers at all levels; build a high-trust remote culture
- Partner with Product on roadmap sequencing and scope trade-offs
- Work directly with customer-facing teams to close feedback loops on model quality
- Communicate AI capabilities and limitations clearly to non-technical stakeholders

Model & Systems Ownership
- Own model performance metrics and drive continuous improvement pipelines
- Build and maintain evals frameworks — regression suites, human review, A/B testing
- Oversee training data collection, curation, and labeling operations
- Manage the full ML lifecycle: experimentation, deployment, monitoring, iteration
- Define and enforce quality bars for agentic workflows entering production

Requirements
Applied AI & Agentic Systems
- Production agentic pipelines using frontier models
- Anthropic SDK · OpenAI SDK · tool use, function calling, multi-agent orchestration
- Reliable agent loop design — planning, memory, tool execution, error recovery
- RAG pipeline design — chunking, embedding models, retrieval tuning, reranking
- Evals frameworks built from scratch — correctness, regression, semantic similarity
- Observability for production AI — tracing, cost tracking, latency, failure analysis

Model Expertise
- Fine-tuning frontier or open-source models for domain-specific tasks
- LoRA, QLoRA, instruction tuning — not just off-the-shelf API calls
- Training data collection, curation, cleaning, and labeling at scale
- LLM inference and serving optimization
- vLLM, TGI, or equivalent
- Model selection trade-offs — cost, latency, capability, context window

Engineering Depth
- Hands-on Python — comfortable writing, reviewing, and shipping production code
- PostgreSQL — schema design, query optimization, indexing strategies
- Distributed systems — async workers, queues, retries, state machines
- Celery or similar async task frameworks is a bonus
- Public-facing API design — REST, versioning, developer experience
- MCP server development — tool-accessible APIs for AI agent integration
- AWS or cloud infrastructure — enough to own AI workload deployments

Ideal Career Background
- Engineering Manager ready for director-level o